Systems and methods for detecting airbag deployment resulting from a vehicle crash

ABSTRACT

Embodiments of the present invention relate to transportation systems. More particularly, embodiments relate to methods and systems for detecting airbag deployment. The method includes obtaining one or more measurements from one or more sensors of a mobile device in a vehicle during a drive, determining a change in pressure based on processing the one or more measurements, and detecting a deployment of one or more vehicle airbags based on the change in pressure.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/416,843, entitled “Systems and Methods for Sensor-Based Vehicle CrashPrediction, Detection, and Reconstruction,” filed on Jan. 26, 2017,which claims the benefit of U.S. Provisional Patent Application No.62/287,251, filed Jan. 26, 2016, entitled “Methods and Systems forPredicting, Detecting and Assessing Vehicle Accidents”, and U.S.Provisional Patent Application No. 62/383,839, filed Sep. 6, 2016,entitled “Systems and Methods for Vehicle Crash Prediction, Detection,and Reconstruction”, the disclosures of which are hereby incorporated byreference in their entireties.

BACKGROUND OF THE INVENTION

Mobile devices, including smart phones, have been utilized to providelocation information to users. Mobile devices can use a number ofdifferent techniques to produce location data. One example is the use ofGlobal Positioning System (GPS) chipsets, which are now widelyavailable, to produce location information for a mobile device. Somesystems have been developed to track driving behaviors including speed,braking, and turn speed. Such systems include external devices that havebeen physically integrated with vehicles to track driving behavior.However, little has been done to help drivers and other interestedparties predict, detect, and reconstruct vehicle accidents (alsoreferred to herein as “crashes” or “collisions”).

SUMMARY OF THE INVENTION

Despite the progress made in relation to collecting data related todrivers and their driving behavior, there is a need in the art forimproved systems and methods related to predicting, detecting, andreconstructing vehicle accidents using a mobile device.

Embodiments of the present invention relate to transportation systems.More particularly, embodiments relate to methods and systems of vehicledata collection by a user having a mobile device. In a particularembodiment, vehicle data, such as vehicle movement data (also termedherein “driving data” or “data”) is collected, analyzed and transformed,and combinations of collected data and transformed data are used indifferent ways, including, but not limited to, predicting, detecting,and reconstructing vehicle accidents.

One important use for collected, analyzed and transformed driving datais the categorization and output of the data. Some embodiments describedherein break down collected driving data into discrete collections ofdata (e.g., trips) and categorize both the whole set of data and thediscrete collections of data. Some embodiments analyze the driving dataand identify specific events that are likely to have occurred during adrive, and the likely occurrence of these events are used by someembodiments to provide additional ways to group, categorize and/oroutput results.

According to some embodiments of the invention, a method of predictingvehicle accidents is provided. The method comprises obtaining aplurality of measurements from one or more sensors of a mobile device ina vehicle during a drive. The method further comprises obtainingcontextual data for the plurality of measurements. The method furthercomprises generating an accident likelihood metric using at least one ofthe plurality of measurements or the contextual data. The accidentlikelihood metric represents a likelihood that the vehicle will beinvolved in an accident during the drive. The method further comprisesdisplaying the accident likelihood metric on the mobile device.

According to some embodiments of the invention, a method of detectingvehicle accidents is provided. The method comprises obtaining aplurality of measurements from a sensor of a mobile device in a vehicleduring a drive. The method further comprises obtaining an accidentthreshold value for the sensor. The accident threshold value representsa minimum value obtained from the sensor that is indicative of anaccident. The method further comprises identifying a measurement in theplurality of measurements that exceeds the accident threshold value. Themethod further comprises determining a fixed duration windowcorresponding to a first portion of the drive. The first portion of thedrive includes a first subset of the plurality of measurements. Thefirst subset of the plurality of measurements includes the identifiedmeasurement. The method further comprises processing the first subset ofthe plurality of measurements to determine a variable duration windowcorresponding to a second portion of the drive within the first portionof the drive. The second portion of the drive includes a second subsetof the first subset of the plurality of measurements. The second subsetalso includes the identified measurement. The method further comprisesanalyzing the second subset to identify an accident in the vehicleduring the drive.

According to some embodiments of the invention, a method ofreconstructing vehicle accidents is provided. The method comprisesobtaining a plurality of measurements from a sensor of a mobile devicein a vehicle during a drive. The method further comprises identifying anaccident in the vehicle during the drive from the plurality ofmeasurements. The method further comprises identifying an accident timeassociated with the accident. The method further comprises at least oneof: (A) analyzing the plurality of measurements obtained before theaccident time to identify at least one prior event, (B) analyzing theplurality of measurements obtained during the accident time to identifyat least one concurrent event, or (C) analyzing the plurality ofmeasurements obtained after the accident time to identify at least onesubsequent event.

Other embodiments of the invention are directed to a system comprising aprocessor and memory coupled to the processor. The memory storesinstructions, which when executed by the processor, cause the system toperform operations including the steps of the above methods.

According to some embodiments, a computer-program product is provided.The computer-program product is tangibly embodied in a non-transitorymachine-readable storage medium of a device. The computer-programproduct includes instructions that, when executed by one or moreprocessors, cause the one or more processors to perform operationsincluding the steps of the above method.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this patent, any or all drawings, and each claim.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the following drawing figures:

FIG. 1 is a simplified system diagram illustrating a driving behaviortracking system using a mobile device according to an embodiment of thepresent invention.

FIG. 2 is a simplified system diagram illustrating a driving behaviortracking system using a server according to an embodiment of the presentinvention.

FIG. 3 is an exemplary user interface for predicting vehicle accidentsaccording to an embodiment of the present invention.

FIG. 4 is an exemplary route on a map that may be used for predictingvehicle accidents according to an embodiment of the present invention.

FIG. 5 is a flowchart illustrating a method of predicting vehicleaccidents according to an embodiment of the present invention.

FIG. 6 is flowchart illustrating a method for determining the likelihoodof events on travel routes according to an embodiment of the presentinvention.

FIG. 7 is a flowchart illustrating a method of identifying occurrencesof events associated with vehicles according to an embodiment of thepresent invention.

FIG. 8 is a simplified diagram of a mobile device in a vehicle accordingto an embodiment of the invention.

FIG. 9 is a flowchart illustrating a method of detecting vehicleaccidents according to an embodiment of the invention.

FIG. 10A is a graph illustrating sensor measurements during an accidentas compared to an accident threshold value according to an embodiment ofthe present invention.

FIG. 10B is a graph illustrating sensor measurements during an accidentwithin a fixed duration window according to an embodiment of the presentinvention.

FIG. 10C is a graph illustrating sensor measurements during an accidentthat have been processed within a fixed duration window according to anembodiment of the present invention.

FIG. 10D is a graph illustrating sensor measurements during an accidentwithin a variable duration window according to an embodiment of thepresent invention.

FIG. 11A is a graph illustrating sensor measurements during an accidenttaken with the mobile device in the left jacket pocket of the driveraccording to an embodiment of the present invention.

FIG. 11B is a graph illustrating sensor measurements during an accidenttaken with the mobile device in the left breast pocket of the driveraccording to an embodiment of the present invention.

FIG. 11C is a graph illustrating sensor measurements during an accidenttaken with the mobile device mounted left of center in the vehicleaccording to an embodiment of the invention.

FIG. 12 is a graph illustrating a plurality of magnetometer measurementstaken during an accident according to an embodiment of the invention.

FIG. 13A is a graph illustrating a plurality of accelerometermeasurements taken during an accident according to an embodiment of theinvention.

FIG. 13B is a graph illustrating accelerometer measurements taken by amobile device during an accident in a mount, in a pants pocket, and inthe driver's hand according to an embodiment of the invention.

FIG. 14 is a graph illustrating a plurality of gyroscope measurementstaken during an accident according to an embodiment of the invention.

FIG. 15 is a flowchart illustrating a method of reconstructing vehicleaccidents according to an embodiment of the invention.

FIG. 16 is a graph illustrating movement of a driver's body after anaccident as recorded by an accelerometer of a mobile device positionedin the left breast pocket of the driver according to an embodiment ofthe invention.

In the appended figures, similar components and/or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Certain aspects and embodiments of this disclosure are provided below.Some of these aspects and embodiments may be applied independently andsome of them may be applied in combination as would be apparent to thoseof skill in the art. In the following description, for the purposes ofexplanation, specific details are set forth in order to provide athorough understanding of embodiments of the invention. However, it willbe apparent that various embodiments may be practiced without thesespecific details. The figures and description are not intended to berestrictive.

The ensuing description provides exemplary embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the exemplary embodimentswill provide those skilled in the art with an enabling description forimplementing an exemplary embodiment. It should be understood thatvarious changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the invention as setforth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional steps notincluded in a figure. A process may correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

The term “computer-readable medium” includes, but is not limited to,portable or non-portable storage devices, optical storage devices, andvarious other mediums capable of storing, containing, or carryinginstruction(s) and/or data. A computer-readable medium may include anon-transitory medium in which data can be stored and that does notinclude carrier waves and/or transitory electronic signals propagatingwirelessly or over wired connections. Examples of a non-transitorymedium may include, but are not limited to, a magnetic disk or tape,optical storage media such as compact disk (CD) or digital versatiledisk (DVD), flash memory, memory or memory devices. A computer-readablemedium may have stored thereon code and/or machine-executableinstructions that may represent a procedure, a function, a subprogram, aprogram, a routine, a subroutine, a module, a software package, a class,or any combination of instructions, data structures, or programstatements. A code segment may be coupled to another code segment or ahardware circuit by passing and/or receiving information, data,arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, or the like.

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, hardware description languages, or anycombination thereof. When implemented in software, firmware, middlewareor microcode, the program code or code segments to perform the necessarytasks (e.g., a computer-program product) may be stored in acomputer-readable or machine-readable medium. A processor(s) may performthe necessary tasks.

Embodiments of the present invention utilize mobile devices to provideinformation on a user's behaviors during transportation. For example, amobile device carried by a user could be used to analyze drivingbehavior, which is of interest for a variety of applications.

As discussed below, some embodiments described herein use approaches tocollecting and analyzing driving data similar to the approachesdescribed in U.S. patent application Ser. No. 15/149,603, filed May 9,2016, entitled “METHODS AND SYSTEMS FOR SENSOR-BASED VEHICLEACCELERATION DETERMINATION,” (the '603 application), U.S. patentapplication Ser. No. 15/149,613, filed May 9, 2016, entitled “METHODSAND SYSTEMS FOR SENSOR-BASED DRIVING DATA COLLECTION” (the '613application), and U.S. patent application Ser. No. 14/749,232, filedJun. 24, 2015, entitled “METHODS AND SYSTEMS FOR ALIGNING A MOBILEDEVICE TO A VEHICLE” (the '232 application), U.S. patent applicationSer. No. 15/249,967, filed Aug. 29, 2016, entitled “METHODS AND SYSTEMSFOR PRESENTING COLLECTED DRIVING DATA,” (the '967 application), U.S.patent application Ser. No. 15/268,049, filed Sep. 16, 2016, entitled“METHODS AND SYSTEMS FOR DETECTING AND ASSESSING DISTRACTED DRIVERS,”(the '049 application), U.S. patent application Ser. No. 15/353,340,filed Nov. 16, 2016, entitled “METHODS AND SYSTEMS FOR COMBINING SENSORDATA TO MEASURE VEHICLE MOVEMENT”, these applications being incorporatedby reference herein in their entireties for all purposes (“theIncorporated Applications”).

Specific examples of the use of different embodiments disclosed in theIncorporated Applications are provided herein, and it is contemplatedthat additional approaches described in these applications can be usedby some embodiments described herein.

FIG. 1 is a simplified system diagram illustrating a system 100 forcollecting and processing vehicle movement data according to anembodiment of the present invention. Some embodiments use vehiclemovement data to detect the occurrence of events associated with thevehicle, e.g., an accident. System 100 includes a mobile device 101having a number of different components. Mobile device 101 includes asensor data block 105, a data processing block 120, and a datatransmission block 130. The sensor data block 105 includes datacollection sensors as well as data collected from these sensors that areavailable to mobile device 101, this can include external devicesconnected via Bluetooth, USB cable, etc. The data processing block 120includes storage 126, and manipulations done to the data obtained fromthe sensor data block 105 includes, but is not limited to, subsampling,filtering, reformatting, etc. Data transmission block 130 includes anytransmission of the data off the phone to an external computing devicethat can also store and manipulate the data obtained from sensor datablock 105.

Some embodiments of the present invention are described using exampleswhere vehicle movement data is collected using mobile devices 101, theseexamples not being limited to any particular mobile device, e.g., avariety of mobile devices include sensors that can detect movement (alsotermed “movement sensors”), such as accelerometers 112, gyroscopes 116,compasses 119, barometers 113. Location determination systems (alsotermed “location sensors”) such as global positioning system (GPS)receivers 110 can also be included. Example mobile devices include smartwatches, fitness monitors, Bluetooth headsets, tablets, laptopcomputers, smart phones, music players, movement analysis devices, andother suitable devices. One of ordinary skill in the art, given thedescription herein, would recognize many variations, modifications, andalternatives for the implementation of embodiments.

To collect data associated with vehicle movement, one or more sensors onmobile device 101 (e.g., the sensors of sensor data block 105) areoperated close in time to a period when mobile device 101 is with thedriver when operating a vehicle—also termed herein “a drive.” With manymobile devices 101, the sensors used to collect data are components ofthe mobile device 101, and use power resources available to mobiledevice 101 components, e.g., mobile device battery power and/or a datasource external to mobile device 101.

Some embodiments use settings of a mobile device to enable differentfunctions described herein. For example, in Apple IOS, and/or AndroidOS, having certain settings enabled can enable certain functions ofembodiments. For some embodiments, having location services enabledallows the collection of location information from the mobile device(e.g., collected by global positioning system (GPS) sensors, andenabling background app refresh allows some embodiments to execute inthe background, collecting and analyzing driving data even when theapplication is not executing.

FIG. 1 also shows a computer server 102 that communicates with mobiledevice 101. In some embodiments, server 102 provides functionality usingcomponents including, but not limited to processor 180. Server 102 alsoincludes data storage 170. It is important to note that, while notshown, one or more of the components shown operating using server 102can operate using mobile device 101. Processor 180 can perform some orall of the processing of the collected movement measurement describedbelow, and direct storage 170 can store the collected movement data, aswell as the results of processing described herein.

To collect data associated with the movement of a vehicle, one or moresensors on mobile device 101 (e.g., the sensors of sensor data block105) are operated close in time to a period when mobile device 101 iswith the driver when operating a vehicle—also termed herein “a drive” or“a trip”. Once the mobile device sensors have collected data (and/or inreal time), some embodiments analyze the data to determine movementmeasurements. As discussed below, some embodiments analyze movementmeasurements (also termed herein “driving data”) to detect events (alsotermed herein “driving events”). Analysis and processing of thecollected data, as described herein, may occur on mobile device 101and/or on server 102.

FIG. 2 shows a system 200 for collecting driving data that can include aserver 201 that communicates with mobile device 101. In someembodiments, server 201 may provide functionality using componentsincluding, but not limited to vector analyzer 258, vector determiner259, external information receiver 212, classifier 214, data collectionfrequency engine 252, and driver detection engine 254. These componentsare executed by processors (not shown) in conjunction with memory (notshown). Server 201 may also include data storage 256. It is important tonote that, while not shown, one or more of the components shownoperating within server 201 can operate fully or partially within mobiledevice 101, and vice versa.

To collect data associated with the driving behavior of a driver, one ormore sensors on mobile device 101 (e.g., the sensors of sensor datablock 105) may be operated close in time to a period when mobile device101 is with the driver when operating a vehicle—also termed herein “adrive” or “a trip”. Once the mobile device sensors have collected data(and/or in real time), some embodiments analyze the data to determineacceleration vectors for the vehicle, as well as different features ofthe drive. Exemplary processes detect and classify driving featuresusing classifier 214, and determine acceleration vectors using vectoranalyzer 258 and vector determiner 259. In some embodiments, externaldata (e.g., weather) can be retrieved and correlated with collecteddriving data.

As discussed herein, some embodiments can transform collected sensordata (e.g., driving data collected using sensor data block 105) intodifferent results to analyze movement measurements and to detect theoccurrence of driving events. Although shown and described as beingcontained within server 201, it is contemplated that any or all of thecomponents of server 201 may instead be implemented within mobile device101, and vice versa. It is further contemplated that any or all of thefunctionalities described herein may be performed during a drive, inreal time, or after a drive.

Examples of collecting driving data using sensors of a mobile device aredescribed herein and in the Incorporated Applications. Examples ofanalyzing collected driving data to detect the occurrence of drivingevents are also described herein and in the Incorporated Applications.

I. Accident Prediction

FIG. 3 depicts a mobile device 101 having an example user interface thathighlights features of some embodiments. Some embodiments use theapproach to assess vehicle movement by having a mobile computing device(e.g., mobile device 101) associated with a driver in a vehicle during adrive. The specifics of the use of movement measurements by embodimentsare discussed with the description of FIG. 8 below, in an accidentdetection description.

The example discussed in FIG. 3 is associated with the prediction of theoccurrence of a vehicle event (also termed herein, an “event” and an“event prediction”) and notification of a driver of an event prediction.In an example use, a driver brings mobile device 101 (also termed a“device” herein) into a vehicle. In an example process similar to theuse of a GPS for trip instructions, the driver specifies a trip route(herein a “travel route” or “route”). As would be appreciated by onehaving skill in the relevant art(s), given the description herein,embodiments can identify a travel route in a variety of ways, e.g.,receiving from another application (e.g., GPS trip directions),retrieving stored routes, and/or other approaches.

As shown in FIG. 3, route 345 is displayed in map 340, with the startingand ending points displayed at 350. Some embodiments display the mode oftravel 360 along with the length of the route, the estimated time tocomplete the route (not labeled), the weather 370 along the route, andthe time of day 330 that the route will be taken. As discussed with FIG.4 below, these values, along with other information, can be used by someembodiments to estimate the likelihood of the driver having a particularevent occur (e.g., a vehicle accident) along the route during the timespecified. In other embodiments, event likelihood estimates can bedetermined using that are less specific to a route and/or a time of day,e.g., the an estimate can be determined that reflects a likelihood ofhaving an event in general that day, on any local road.

Once determined, likelihood values can be displayed for the driver on anapplication running on the mobile computing device (e.g., Accident Risk320 displayed). In addition, some embodiments can notify a user of thepotential for distracted drivers 380 along route 345. This potential canbe determined, for example, from other instances of embodimentsexecuting in other vehicles traveling along route 345. Using approaches,described for example in the Incorporated Applications, for determiningdistracted drivers, some embodiments can aggregate driving informationcollected from other drivers while maintaining the privacy ofinformation collected and/or determined by embodiments. For convenience,as used herein, inattentiveness, distraction, failing to pay attention,and/or other similar terms and phrases broadly signify a driver notpaying proper attention to tasks associated with safely operating thevehicle.

A. Example Factors Used to Predict Accidents

With an identified route 345, some embodiments identify one or more ofthe following items A1-A9 for use in determining the likelihood of anevent (e.g., a vehicle accident) occurring. For convenience, a vehicleaccident is referenced herein as an event considered by someembodiments, but it should be noted that the discussion of vehicleaccident events can also apply to other types of events.

In some embodiments, some of items A1-A9 are identified after beingreceived from an external information source. An additional discussionof using external data sources is found in the Incorporated Applications(e.g., grading the severity of a hard braking event was bycross-referencing the event with the weather, time of day, school zone,etc.). Items A1-A9 are as follows:

A1. The Time and Day of the Year/Week Route 345 Will be Used:

Knowing the time of day that a drive will occur allows some embodimentsto estimate the likelihood of a vehicle accident consideringtime-associated factors, e.g., morning sleepiness, after lunch lethargy,night-time intoxicated drivers, and/or the effects of darkness ondriving safety. Using the time of the year allows some embodiments toconsider holiday times, and/or seasons of the year. Using the day of theweek enables some embodiments to consider “Monday blues,” Fridayafternoon eagerness and/or Saturday night intoxicated drivers, and theincreased risks these factors cause to individual drivers.

A2. Weather Forecasts During Time 330 for Areas Along the Route 345:

This information helps to determine the risk of a vehicle accident byconsidering the potential for adverse weather conditions, e.g., snow,ice, high winds, flooding, etc. In some embodiments, the forecast may betailored to the expected time interval of the drive, e.g., a forecastfor starting point 305 at 9:32 AM and a forecast for ending point 306for 10:15 AM (these example times shown in FIG. 3 at time 330). Theseweather forecasts can be included in the user interface, e.g., weather370.

A3. Historical Weather Information for the Route:

This information helps to fill in gaps in weather forecast A1, and/oraccount for unexpected weather conditions.

A4. Traffic Congestion Predicted to be on Route 345:

This information helps to determine the risk of a vehicle accident byconsidering the amount of vehicles on the route, frustration level ofdrivers, and/or other negative effects that would be known to one havingskill in the relevant art(s), given the description herein.

A5. The Presence of Construction Along Route 345:

Similar to traffic congestion (A4), this factor can affect the accidentrisk on the route.

A6. Historical Accident Data for Route 345:

In some embodiments, historical accident data can be used, alone, oralong with other factors, to estimate the likelihood of a futureaccident. This data can be used, by some embodiments, to identifyspecific locations along the route that have a relatively higherlikelihood of a vehicle accident occurring.

A7. Features of Route 345:

Some embodiments receive information about route features (curves,hills, intersections, bridges, and/or other features), which can be usedto estimate the likelihood of an accident along the route. Thesefeatures can also include aspects that are near route 345, e.g., thepopulation and demographics of the neighborhoods on route 345, and/orthe amount of commercial activity in an area.

A8. Occurrences of Distracted Driving by Others:

As noted above, some embodiments can aggregate information among severalinstances of embodiments. For example, the detection of distracteddriving behavior along the route by other drivers can indicate a higherlikelihood of accidents on the route. It should be appreciated that anyother detected behavior by other drivers can also be aggregated and usedto estimate risk, e.g., hard stops and acceleration, excessive lateralacceleration on turns (also termed “sharp” turns), and/or other drivingbehaviors.

A9. Patterns Associated with Route 345 and/or the Driver:

In some embodiments, one or more of the factors discussed herein can beanalyzed to determine whether any patterns of features exist that appearto lead to accidents. For example, a pattern could be: when it's snowing(A2) on a congested road (A4) after dark (A1), a distracted driver (A8)has a relatively high likelihood of having an accident on a particularturn (A7). In addition to using the combination of features in adetermination of the likelihood of an accident occurring, someembodiments can select one or more of the pattern components tohighlight to the driver. For the above pattern, a driver could benotified to “pay extra attention today,” and/or “be careful in thesnow.” In addition, a marker could be placed on map 340 to highlightdifferent pattern elements, e.g., a type of dangerous turn that aparticular driver has had difficulty with in the past.

One having skill in the relevant art(s), given the description herein,will appreciate the broad variety of additional data sources can becombined with other data to estimate the likelihood of a vehicleaccident in different circumstances.

To provide additional information for predicting vehicle accidents alongroute 345, some embodiments identify additional items of informationabout the driver of the vehicle. The following items B1-B4 are used bysome embodiments to determine the likelihood of a vehicle accidentoccurring. In some embodiments, some of B1-B4 are identified after beingreceived from an external information source, and in some embodimentssome of B1-B4 are determined based information stored within server 102and/or server 201.

B1. Past General Driving Behavior of the Driver:

As described in the Incorporated Applications, driving behavior can becollected by some embodiments, e.g., using the sensors of mobile device101. For example, the driver may have rapid starts and stops, exceedspeed limits, and have lateral acceleration measurements that exceedthresholds, one or more of which can be indicative of a driver with ahigher likelihood of a vehicle accident. General driving behaviordescribes behavior of a user over different types of routes.

B2. Driving Behavior of the Driver Over Route 345:

In some embodiments, when a driver has driven route 345 before, thedriving behavior of the driver is analyzed, and this is included in ananalysis of future vehicle accidents for this driver on the route. Forexample, route 345 may include a difficult turn that has causedaccidents for other drivers in the past. In this example, when thedriver drives route 345 in the past, data was collected that isindicative of safe driving behavior on this turn. Thus, when thelikelihood of a vehicle accident for route 345 is estimated, thesignificance of this turn can be reduced based on the previous safebehavior.

B3. Demographics of the Driver:

Driver demographics, e.g., gender, age, marital status, etc. can affectthe likelihood of an accident occurring.

B4. Past Distracted Driving Events of the Driver:

As discussed above, and in the Incorporated Applications, someembodiments can detect distracted driving by the driver. This distracteddriving can be used by some embodiments to predict future accidents.

FIG. 4 depicts a more detailed example of map 340 having route 345 fromFIG. 3. An example route 345 from points 405 to 406 is shown in anexample graphical user interface (GUI) with display elements used bysome embodiments. Illustrating processes discussed with FIG. 3 above,FIG. 4 shows geographic area 340 with a route 345 having starting point405 and ending point 406. In some embodiments, starting point 405 isidentified by GPS in the mobile device 101, and ending point 406 isidentified as the end of the identified route.

Some embodiments identify one or more items of data described above(e.g., A1-A9 and B1-B4) and use a combination of the items to estimatethe probability (also termed herein, “likelihood”) of an event (e.g., avehicle accident) occurring. One having skill in the relevant art(s),given the description herein, would appreciate how statistical analysisis used by some embodiments to determine the likelihood of a vehicleaccident generally during travel along route 345, and/or specific pointsalong route 345.

In some embodiments, points along route 345 that have a relatively highlikelihood of accidents (e.g., a likelihood above a threshold), can belabeled as danger points. On the depicted drive in FIG. 4, danger points410A and 410B are shown. In addition, a highlighted area 460 is shownwith a distinctive color in the GUI. In this example, this whole region460 is determined to have a higher probability of accidents, e.g.,because of detected distracted driving along this segment of road,dangerous road conditions, and/or road construction.

FIG. 5 is a flowchart 500 illustrating a method of predicting vehicleaccidents according to an embodiment of the invention. At step 505, aplurality of measurements are obtained from sensors of a mobile devicein a vehicle during a drive. The mobile device may be, for example,mobile device 101 of FIG. 1. The sensors may include any or all of thesensors contained in the mobile device. In some embodiments, themeasurements may include GPS measurements.

At step 510, contextual data for the plurality of measurements isobtained. The contextual data may include, for example, weather data forthe location where the plurality of measurements were obtained, roadquality data for the road on which the plurality of measurements wereobtained, traffic data for the road on which the plurality ofmeasurements were obtained, unsafe driving path indicators for the roadon which the plurality of measurements were obtained (e.g., based onhistorical accident rates, reported intensities of historical accidents,unsafe road characteristics such as steep hills or curves, etc.), unsafedriving time indicators for the time at which the plurality ofmeasurements were collected (e.g., at night, at busy times such asholidays or rush hour, etc.), and the like. In some embodiments,contextual data may include a recommended route (that has not yet or iscurrently being taken) to a destination on a GPS.

At step 515, an accident likelihood metric is generated using at leastone of the plurality of measurements or the contextual data. Theaccident likelihood metric represents a likelihood that the vehicle willbe involved in an accident during the drive, either on the whole, atparticular points, during particular actions (e.g., left turns), and/oron particular segments. The accident likelihood metric may berepresented by any combination of letters, numbers, and/or symbols thatindicate the probability of an accident occurring. For example, theaccident likelihood metric could be represented by a scale between0-100, a letter between A-F, a colored light (e.g., green, yellow, red),a smiling face versus a frowning face, etc., and combinations thereof.

In some embodiments, the accident likelihood metric is generated usingonly the plurality of measurements. For example, the accident likelihoodmetric may be generated based on sensed variation in lateral vehicleposition (e.g., to determine if a vehicle is going off of the road),rumble strip detection (e.g., to determine if a vehicle is going off ofthe road), frequent and/or hard braking (e.g., indicative of heavycongestion and/or not keeping the proper distance from vehicles in frontof the driver), distracted driving (e.g., sensed driver interaction withthe mobile device while the vehicle is in motion), and the like.

In some embodiments, the accident likelihood metric is generated usingonly the contextual data. For example, the accident likelihood metricmay be generated based on weather conditions (e.g., that impairvisibility, that impair vehicle control, etc.), road quality (e.g., abumpy road under construction), traffic alerts (e.g., particularly heavytraffic), an unsafe driving path (e.g., driving through a high accidentintersection), an unsafe driving time (e.g., driving in the middle ofthe night), and the like.

In some embodiments, the accident likelihood metric is generated usingboth the plurality of measurements and the contextual data. For example,the accident likelihood metric may analyze both the weather (e.g., rainyweather) and any sensed driving adjustments due to the weather (e.g.,use of windshield wipers, lowered speed, etc.). In another example, theaccident likelihood metric may consider a traffic alert indicating lighttraffic, a sensed high rate of speed, and multiple sensed hard brakingevents, to conclude a high likelihood of an accident due to the driverfollowing too closely.

In some embodiments, the accident likelihood metric may take intoaccount driver specific data obtained from sources internal or externalto the system described with respect to FIG. 1. This embodiment may beimplemented alone or in combination with any of the embodimentsdescribed above. For example, the accident likelihood metric may takeinto account the past general driving behavior of the driver (e.g., pastrapid accelerations, past hard brakes, past speeding, past distracteddriving, etc.), past driving behavior of the driver on the same route(e.g., past safe turns at a high accident intersection), demographics ofthe driver (e.g., gender, age, glasses or contact lens wearer, etc.),and the like.

At step 520, the accident likelihood metric is displayed on the mobiledevice. In some embodiments, the accident likelihood metric is displayedgraphically on a graphical user interface of the mobile device. In someembodiments, the accident likelihood metric is announced audibly (e.g.,with words or an alarm) so as to avoid driver distraction or interactionwith the mobile device. In some embodiments, the accident likelihoodmetric is conveyed tangibly, such as by vibrating when it is above acertain threshold.

FIG. 6 is a simplified flowchart showing a method for determining thelikelihood of events for a vehicles traveling on travel routes. Method600 begins at block 610 where a location measurement is obtained from alocation sensor of a mobile device in a vehicle. In some embodiments, alocation measurement (e.g., starting point 405) is obtained from alocation sensor (e.g., GPS receiver 110) of a mobile device (e.g.,mobile device 101) in a vehicle (e.g., vehicle 850 discussed below).

In block 620, a travel route is identified for the vehicle, the travelroute beginning at a location indicated by the location measurement,having a plurality of waypoints, and ending at an end point. In someembodiments, a travel route (e.g., route 345) beginning at a locationindicated by the location measurement (e.g., starting point 405), havinga plurality of waypoints (e.g., danger points 410A and 410B), and endingat an end point (e.g., ending point 406).

In block 630, one or more past events associated with one or more ofwaypoints of the plurality of waypoints are identified, resulting in adataset of past events. In some embodiments, one or more past eventsassociated with one or more of waypoints of the plurality of waypointsare identified (e.g., factors A1-A9 retrieved from an external source byserver 102), resulting in a dataset of past events (e.g., stored in datastorage 170).

In block 640, risk information associated with the vehicle and themobile device is identified. In some embodiments, risk informationassociated with the vehicle and the mobile device is identified (e.g.,processor 180 can determine risk information based on data stored indata storage 170).

In block 650, a likelihood of occurrence of a future event on the travelroute is estimated, the future event associated with the vehicle and themobile device, the estimating based on the dataset of past events andthe risk information. In some embodiments, a likelihood of occurrence ofa future event on the travel route is estimated, the future eventassociated with the vehicle and the mobile device (e.g., mobilecomputing device 101), the estimating based on the dataset of pastevents and the risk information (e.g., by processor 180 using datastored in data storage 170).

II. Accident Detection

FIG. 7 is a simplified flowchart of a method of identifying occurrenceof events associated with vehicles on travel routes. Method 700 beginsat block 710 where a collection of movement measurements is obtainedfrom a mobile device in a vehicle, each movement measurement associatedwith a geographic location. In some embodiments, a collection ofmovement measurements is obtained from a mobile device (e.g., sensors ofmobile device 101) in a vehicle (e.g., vehicle 850), each movementmeasurement associated with a geographic location (e.g., points alongroute 345).

In block 720, a deceleration threshold is identified. In someembodiments, a deceleration threshold is identified (e.g., by processor180 in computer server 102).

In block 730, a deceleration measurement is determined for a firstsubset of the collection of movement measurements. In some embodiments,the deceleration measurement is determined for a first subset of thecollection of movement measurements (e.g., processor 180 analyzedmovement measurements collected by sensors of mobile computing device101).

In block 740, a first event associated with the first subset isidentified when the deceleration measurement exceeds the decelerationthreshold. In some embodiments, a first event associated with the firstsubset is identified when the deceleration measurement exceeds thedeceleration threshold (e.g., by processor 180 of server 102).

FIG. 8 is a simplified diagram 800 of a mobile device 101 in a vehicle850, according to an embodiment. FIGS. 6 and 7 provide examples ofdifferent types of processes, used by some embodiments, to collect andanalyze movement measurements from mobile device 101. FIG. 8 depicts avehicle 850 having a driver (not shown) where mobile device 101 is usedto provide movement measurements that enable detection and assessment ofvehicle accidents.

In some embodiments, as described in the Incorporated Applications,using an extended Kalman filter applied to movement data from mobiledevice sensors, a gravity vector (e.g., gravity vector 840) for a mobiledevice (e.g., mobile device 101) in a moving vehicle (e.g., vehicle 850)moving in direction 860.

As shown in FIG. 8, vehicle 850 is traveling along vector 860, withforces measured by mobile device 101 measuring the movement of thevehicle. Mobile computing device 101 can measure a variety of differentforces, e.g., forward acceleration 820 and lateral acceleration 830.

Mobile device 101 sensors can be used to detect accidents in a varietyof ways. According to embodiments of the present invention, the mobiledevice can measure events that have a large spike in acceleration and/ordeceleration, for example, measured using GPS speed. As an example, forlarge accidents, the accelerometer output is scanned as a function oftime to determine time stamps for which there are spikes in theacceleration of the mobile device accelerometer associated with speeddecreases from above a threshold (e.g., 20 mph) to a speed close tozero. After the sudden deceleration, the speed remains near zero for anextended period of time, indicating that the car is immobilized inaccordance with a large scale accident.

Because the mobile device is not fixed to the vehicle, theacceleration/deceleration patterns will be unique to the mobile device.As an example, in contrast with an accelerometer mounted in the vehicle,during an accident in which the mobile device is lying on the seat, thedeceleration for the mobile device can lag the deceleration of thevehicle since the mobile device will move from the seat to an interiorsurface of the vehicle (e.g., a door or the firewall) before coming to astop. As a result, the timing of the acceleration patterns and theircharacteristics will be unique to a mobile device. Moreover, theorientation of the mobile device can be used to map the accelerationsmeasured using the mobile device to the frame of reference of thevehicle. The reference frame of the mobile device can be converted intothe reference frame of the vehicle and then acceleration changes can beanalyzed to detect an accident. The combination of the speed data andthe accelerometer data can be utilized to filter out largeaccelerations/decelerations in which the vehicle speed does not changein a manner consistent with the large measured accelerations. An examplewould be hard braking that results in the phone sliding off the seat andcrashing into the firewall, thereby generating a large acceleration, butthe vehicle continues to move forward after the sudden stop.

Items C1-C4 provide examples of movement measurements that areindicative of different types of accidents:

C1.

If vehicle 850 is stopped, and is struck from behind by another vehicle(e.g., along vector 860), sensors will initially detect no acceleration,then, most likely, a relatively high acceleration rate that vehicle 850is unable to cause by normal driving forces. This acceleration reflectsvehicle 850 being pushed ahead very quickly by the other vehicle.

C2.

If vehicle 8550, as a result of the rear-end accident described above ispushed along vector 867 into vehicle 865, initially sensors will detectno acceleration, then, a relatively rapid acceleration forward followedby a relatively rapid deceleration. Using this information, someembodiments can determine whether another vehicle struck vehicle 850first and pushed vehicle 850 into vehicle 865. Some embodiments can alsodetermine whether vehicle 850 struck vehicle 865 first, then vehicle 850was struck by a vehicle from behind. One having skill in the relevantart(s), given the description herein, would appreciate that theinformation regarding the order that the three vehicles collided can bevery valuable to the driver of device 800.

C3.

When vehicle 850 is struck at an angle, sensors can detect variousresults that can occur, e.g., a large jolt, then a spinning motion.

C4.

When vehicle 850 is involved in a severe accident, often front, and/orside airbags may deploy. Some embodiments use sensors to first detectrelatively rapid deceleration consistent with an accident. After theinitial deceleration is detected, a distinctive “airbag deployment” setof forces are detected by sensors of mobile device 101.

FIG. 9 is a flowchart 900 illustrating a method of detecting vehicleaccidents according to an embodiment of the invention. At step 905, aplurality of measurements are obtained from a sensor of a mobile devicein a vehicle during a drive. The mobile device may be, for example,mobile device 101 of FIG. 1. The sensor may be any of the sensorscontained in mobile device.

At step 910, an accident threshold value for the sensor is obtained. Theaccident threshold value represents a minimum value obtained from thesensor that is indicative of an accident. For example, the accidentthreshold value for an accelerometer may be 2 m/s². In another example,the accident threshold value for a magnetometer may be 70 microtesla. Instill another example, the accident threshold value for a gyroscope maybe 2.5 radian/second. In other words, the accident threshold value maybe different depending on the type of sensor that is collecting themeasurements during the drive, as each type of sensor may observe adifferent change in value during an accident.

FIG. 10A is a graph illustrating sensor measurements during an accidentas compared to an accident threshold value according to an embodiment ofthe present invention. In FIG. 10A, the accident threshold value is foran accelerometer. The accident threshold value is set at an accelerationvalue of approximately 2. For the accident illustrated in FIGS. 10A-10D,the phone was located in the left pant pocket of a passenger in thefront seat and the car was oriented toward the front during the accidentso that the impact was with the front of the car.

Turning back to FIG. 9, at step 915, a measurement is identified in theplurality of measurements that exceeds the accident threshold value.With respect to FIG. 10A, the measurement may be a measurement ormultiple measurements exceeding the accident threshold value betweenapproximately 2.2 and 2.4 seconds. In some embodiments, measurementsfrom a magnetometer, accelerometer, and/or a gyroscope may exceed theaccident threshold.

Turning back to FIG. 9, at step 920, a fixed duration window isdetermined that corresponds to a first portion of the drive. The firstportion of the drive includes a first subset of the plurality ofmeasurements, the first subset including the measurement(s) identifiedat step 915. In other words, a fixed duration of time (e.g., 5 seconds,10 seconds, etc.) is determined that captures the measurement(s)identified as exceeding the accident threshold value, such that most orall of the measurements taken during the accident are within the fixedduration of time.

FIG. 10B is a graph illustrating sensor measurements during an accidentwithin a fixed duration window 1005 according to an embodiment of thepresent invention. As shown in FIG. 10B, the fixed duration window 1005captures approximately 3.4 seconds of the plurality of measurements.Further, the fixed duration window 1005 appears to capture all of theaccelerometer measurements above the accident threshold value. In otherembodiments, however, the fixed duration window 1005 may be of anylength, such as, for example, 10 seconds before and after a detectedaccident.

Turning back to FIG. 9, at step 925, the first subset of the pluralityof measurements within the fixed duration window 1005 is processed. FIG.10C is a graph illustrating sensor measurements during an accident thathave been processed within the fixed duration window 1005 according toan embodiment of the present invention.

Turning back to FIG. 9, at step 925, a variable length windowcorresponding to a second portion of the drive is determined. The secondportion of the drive is within the first portion of the drive, i.e., thesecond portion of the drive is fully encompassed by the first portion.The second portion of the drive includes a second subset of theplurality of measurements. The second subset is a subset of the firstsubset, i.e., the second subset is fully encompassed by the firstsubset. The second subset also includes the measurement(s) identified atstep 915. In other words, a variable length of time is determined thatcaptures the measurement(s) identified as exceeding the accidentthreshold value, such that most or all of the measurements taken duringthe accident are within the variable duration of time, while excludingmost or all of the measurements not associated with the accident. Thus,the variable duration window is often smaller than the fixed durationwindow. FIG. 10D is a graph illustrating sensor measurements during anaccident within a variable duration window 1010 according to anembodiment of the present invention.

Turning back to FIG. 9, at step 930, the second subset of the pluralityof measurements corresponding to the variable duration window isanalyzed to identify an accident in the vehicle during the drive. Oncean accident in the vehicle has been identified, one or more changes maybe implemented by mobile device 101. For example, the plurality ofmeasurements may be made at a first frequency before the accident isdetected and at a second frequency after the accident is detected. Inone example, the second frequency may be lower than the first frequency,as the vehicle will likely no longer be moving after the accident. Inanother example, an identified accident in the vehicle may be used toadjust a calculated driver score associated with the driver, asdescribed further in U.S. Provisional Patent Application No. 62/346,013,filed Jun. 6, 2016, entitled “SYSTEMS AND METHODS FOR SCORING DRIVINGTRIPS”, herein incorporated by reference in its entirety. In stillanother example, the measurements indicative of the accident may beprovided to a central server (e.g., server 201), and the central servermay use the accident measurements to train itself by improving futuremodels and thresholds used to identify accidents. In this example, theuser involved in the suspected accident may be prompted to confirm thatan accident occurred on the mobile device prior to using the accidentmeasurements to improve future models.

The sensors of the mobile device can be used to identify accidents in avariety of ways. According to embodiments of the present invention, themobile device can measure events that have a large spike in accelerationand/or deceleration, for example, measured using GPS speed. As anexample, for large accidents, the accelerometer output is scanned as afunction of time to determine time stamps for which there are spikes inthe acceleration of the mobile device accelerometer associated withspeed decreases from above a threshold (e.g., 20 mph) to a speed closeto zero. After the sudden deceleration, the speed remains near zero foran extended period of time, indicating that the car is immobilized inaccordance with a large scale accident.

Because the mobile device is not fixed to the vehicle, theacceleration/deceleration patterns will be unique to the mobile device.As an example, in contrast with an accelerometer mounted in the vehicle,during an accident in which the mobile device is lying on the seat, thedeceleration for the mobile device can lag the deceleration of thevehicle since the mobile device will move from the seat to an interiorsurface of the vehicle (e.g., a door or the firewall) before coming to astop. As a result, the timing of the acceleration patterns and theircharacteristics will be unique to a mobile device. Moreover, theorientation of the mobile device can be used to map the accelerationsmeasured using the mobile device to the frame of reference of thevehicle. The reference frame of the mobile device can be converted intothe reference frame of the vehicle and then acceleration changes can beanalyzed to detect an accident. The combination of the speed data andthe accelerometer data can be utilized to filter out largeaccelerations/decelerations in which the vehicle speed does not changein a manner consistent with the large measured accelerations. An examplewould be hard braking that results in the phone sliding off the seat andcrashing into the wall, thereby generating a large acceleration, but thevehicle continues to move forward after the sudden stop.

For example, FIG. 11A is a graph illustrating sensor measurements duringan accident taken with the mobile device in the left jacket pocket ofthe driver according to an embodiment of the present invention. For theaccident illustrated in FIG. 11A, the phone was located in the leftjacket pocket of the driver and the car was oriented toward the front.FIG. 11A illustrates measurements from a gyroscope, an accelerometer,and a magnetometer with an accident occurring at approximately 1 second.FIG. 11B is a graph illustrating sensor measurements during an accidenttaken with the mobile device in the left breast pocket of a rear seatpassenger with the car oriented towards the front according to anembodiment of the present invention. FIG. 11B illustrates measurementsfrom a gyroscope, an accelerometer, and a magnetometer with an accidentoccurring at approximately 1.2 seconds. FIG. 11C is a graph illustratingsensor measurements during an accident taken with the mobile devicemounted on a window left of center in the vehicle with the car being hitfrom the rear according to an embodiment of the invention. FIG. 11Cillustrates measurements from a gyroscope, an accelerometer, and amagnetometer with an accident occurring at approximately 1.3 seconds.

FIG. 12 is a graph illustrating a plurality of magnetometer measurementstaken by a plurality of mobile devices during accidents according to anembodiment of the invention. As shown in FIG. 12, an accident may beidentified by a change in magnetometer signal due to the mobile devicechanging orientation (e.g., if it is loose or unsecure). In addition,the final resting magnetometer signal may change after an accident basedon a change in location of the mobile device in the vehicle, a change invehicle materials around the mobile device, a change in objectssurrounding the mobile device, and the like. Thus, the change in themagnetometer signal is the important factor to consider in determiningwhether an accident has occurred, and not the absolute values. In thissense, the derivative of the magnetometer signal may be particularlyuseful. Other data can be collected by the magnetometer before, during,or after an accident, such as whether other vehicles are passing rapidly(e.g., the vehicle in the accident is stopped on the shoulder), whetherwindshield wipers were being used at the time of the accident, etc.

FIG. 13A is a graph illustrating a plurality of accelerometermeasurements taken by a plurality of mobile devices during accidentsaccording to an embodiment of the invention. The accelerometermeasurements are all centered in FIG. 13A such that the accidentsoccurred at a time of 0.0 seconds. As shown in FIG. 13A, an accident maybe identified by an acceleration spike that is caused when an accidentoccurs. Another identifying feature of an accident as recorded by amobile device is weightlessness, i.e., when the mobile device goesflying through the air, the acceleration detected on it is 0 (whereas itis usually observed at gravity or 9.809). To determine the magnitude ofan accident, measurements taken by the mobile device must take intoaccount accident intensity from when the mobile device hits a softsurface (e.g., the back of the front seat) versus a hard surface (e.g.,the dashboard or a window). This can be determined by using the durationof the signal of the mobile device hitting the surface. Initial (andmuch smaller) secondary movement may indicate if the mobile device hitsomething or stayed secure, which changes the magnitude of theacceleration signal.

FIG. 13B is a graph illustrating accelerometer measurements taken by amobile device during an accident in a mount, in a pants pocket, and inthe driver's hand according to an embodiment of the invention. Mobiledevices that are mounted or in the hand of the driver often times havelong durations of time at low accelerations because they are flyingthrough the air and not experiencing the effect of gravity, whereasmobile devices that are well secured (e.g., in a pants pocket) do notstay at low values for very long. Thus, accelerometer measures can varyover a large range as a result of the phone location.

FIG. 14 is a graph illustrating a plurality of gyroscope measurementstaken by a plurality of mobile devices during accidents according to anembodiment of the invention. The gyroscope measurements are all centeredin FIG. 8 such that the accidents occurred at a time of 0.0 seconds. Asshown in FIG. 14, accidents may be characterized from gyroscopemeasurements by certain characteristics of the signal. For example,gyroscope measurements may indicate that the mobile device is spinningthrough the air at a constant rate, which rarely happens in natural useof a mobile device.

Various other sensors of the mobile device may be used to identify anaccident as well. In one example, a barometer or pressure sensor may beused to detect a change in pressure caused by airbag deployment. Inanother example, a microphone may be used to detect airbag deploymentand/or the distinctive metal-on-metal sound cause by an accident. Instill another example, a GPS device may be used to confirm that thevehicle is not moving after a detected accident (e.g., the vehicle haspulled over to the side of the road, the vehicle has become disabled,etc.).

Upon detection of an accident, various follow-up steps may be takenaccording to some embodiments. For example, embodiments of the inventionmay provide for automatic contacting of first responders, tow trucks,emergency contacts, etc. In another example, relevant information may bepushed to the mobile device for display to the driver. Such relevantinformation may include common preventative measures for whiplash, if itis detected that a rear collision occurred at a certain intensity.

III. Accident Reconstruction

In some embodiments, after an accident is detected by processesdescribed above, different processes can be performed that assess andreconstruct the accident. As discussed above, in the process ofdetecting the occurrence of an accident, movement information of thevehicle is collected and analyzed. For example, accidents are evaluatedby some embodiments based on the severity of the forces measured. Inaddition, some embodiments may analyze the direction of forces measuredto determine a type of accident, e.g., a broadside accident, a rear-endaccident, and/or a roll-over accident, as described further in theIncorporated Applications.

FIG. 15 is a flowchart 1500 illustrating a method of reconstructingvehicle accidents according to an embodiment of the invention. At step1505, a plurality of measurements are obtained from a sensor of a mobiledevice in a vehicle during a drive. The mobile device may be, forexample, mobile device 101 of FIG. 1. The sensor may include any sensorcontained in the mobile device. The plurality of measurements may bealigned to the vehicle's reference frame before being used for furtheranalysis, as described by U.S. patent application Ser. No. 14/749,232,filed Jun. 24, 2015, entitled “METHODS AND SYSTEMS FOR ALIGNING A MOBILEDEVICE TO A VEHICLE”, herein incorporated by reference in its entirety.

At step 1510, an accident in the vehicle during the drive is identifiedfrom the plurality of measurements. The accident may be identified, forexample, according to any of the methods described herein. At step 1515,an accident time associated with the accident is identified. Forexample, the accident time may be between 9:51:01 AM and 9:51:03 AM.

Following step 1515, at least one of steps 1520A-C may occur. At step1520A, the plurality of measurements obtained during the drive beforethe accident time (e.g., prior to 9:51:01 AM) may be analyzed toidentify at least one prior event (i.e., at least one event occurringprior to the accident). Thus, some embodiments may analyze differentfactors leading up to the accident. External information (e.g., weather,road conditions, traffic, etc.) can be combined with detected drivingbehaviors (e.g., excessive lateral acceleration, hard braking, rapidacceleration, distracted driving, changing lanes, etc.) to providepotential causes of the accident. For example, the plurality ofmeasurements and/or external information may be used to determine thatthe driver was driving at 23 mph going north when he slammed on hisbrakes, swerved left and crashed into a barrier at 45 degrees. Theplurality of measurements and/or external information may further beused to determine that the driver was not talking or texting on themobile device, not exhibiting dangerous behavior such as aggressive lanechanges, and was going below the speed limit while it was raining. Insome embodiments, the period of time before the accident time which isanalyzed may correspond to a fixed or variable length window that may bedetermined by any method.

At step 1520B, the plurality of measurements obtained during the driveduring the accident time (e.g., between 9:51:01 AM and 9:51:03 AM) maybe analyzed to identify at least one concurrent event (i.e., at leastone event occurring during the accident). Thus, some embodiments mayanalyze what happened during the accident. For example, some embodimentscan detect the deployment of different vehicle airbags (e.g., sideand/or front), and this information can provide additional informationabout the accident (e.g., severity, type of accident, etc.). Forexample, the plurality of measurements may be used to determine at whichangle the vehicle hit or was hit, whether a head on collision occurred,whether the vehicle was rear ended, whether the vehicle rear endedanother vehicle, whether the vehicle was T-boned or got T-boned, whetherthe vehicle hit a barrier on the side of the road, whether the vehiclespun out, and the like.

At step 1520C, the plurality of measurements obtained during the driveafter the accident time (e.g., after 9:51:03 AM) may be analyzed toidentify at least one subsequent event (i.e., at least one eventoccurring after the accident). Thus, some embodiments may analyze whathappened after the accident. For example, some embodiments can detectthat the vehicle moved to the side of the road, was disabled andblocking a lane of traffic, rolled over after impact, was hit again byanother vehicle after the initial impact, and the like. In someembodiments, the period of time after the accident time which isanalyzed may correspond to a fixed or variable length window that may bedetermined by any method.

FIG. 16 is a graph illustrating movement of a driver's body before,during, and after an accident as recorded by an accelerometer of amobile device positioned in the left breast pocket of the driveraccording to an embodiment of the invention. These measurements may beused to determine magnitude of impact on the driver (or particular partsof the driver) and potential injury to the driver, as the mobile deviceis essentially fixed to the driver. As shown in FIG. 16, movement of thebody back and forth may be observed from the accelerometer of the mobiledevice.

The accident reconstruction data described herein may be used by avariety of parties. For example, the accident reconstruction data may beused by insurance companies to determine liability for the accident. Inanother example, the accident reconstruction data may be used by policein reconstructing a fatal accident and/or determining fault for theaccident.

As noted, the computer-readable medium may include transient media, suchas a wireless broadcast or wired network transmission, or storage media(that is, non-transitory storage media), such as a hard disk, flashdrive, compact disc, digital video disc, Blu-ray disc, or othercomputer-readable media. The computer-readable medium may be understoodto include one or more computer-readable media of various forms, invarious examples.

In the foregoing description, aspects of the application are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the invention is not limited thereto. Thus,while illustrative embodiments of the application have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. Various features and aspects of theabove-described invention may be used individually or jointly. Further,embodiments can be utilized in any number of environments andapplications beyond those described herein without departing from thebroader spirit and scope of the specification. The specification anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive. For the purposes of illustration, methods were described ina particular order. It should be appreciated that in alternateembodiments, the methods may be performed in a different order than thatdescribed.

Where components are described as performing or being “configured to”perform certain operations, such configuration can be accomplished, forexample, by designing electronic circuits or other hardware to performthe operation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software,firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purposes computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas random access memory (RAM) such as synchronous dynamic random accessmemory (SDRAM), read-only memory (ROM), non-volatile random accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates program code in the form of instructions or datastructures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured for encodingand decoding, or incorporated in a combined video encoder-decoder(CODEC).

The examples and embodiments described herein are for illustrativepurposes only. Various modifications or changes in light thereof will beapparent to persons skilled in the art. These are to be included withinthe spirit and purview of this application, and the scope of anyincluded claims.

What is claimed is:
 1. A method of detecting airbag deployment, themethod comprising: obtaining one or more measurements from one or moresensors of a mobile device in a vehicle during a drive; determining achange in pressure based on processing the one or more measurements; anddetecting a deployment of one or more vehicle airbags based on thechange in pressure.
 2. The method of claim 1 wherein the one or moremeasurements comprise a measurement of pressure inside the vehicle anddetermining the change in pressure is based on the measurement ofpressure inside the vehicle.
 3. The method of claim 2 wherein the one ormore sensors comprise a pressure sensor and the measurement of pressureinside the vehicle is obtained from the pressure sensor.
 4. The methodof claim 3 wherein the pressure sensor comprises a barometer.
 5. Themethod of claim 1 wherein detecting the deployment of the one or morevehicle airbags comprises detecting a deployment of an airbag at afront-left side of the vehicle.
 6. The method of claim 1 whereindetecting the deployment of the one or more vehicle airbags is based onthe change in pressure exceeding a pressure change threshold value. 7.The method of claim 1 further comprising generating an accidentlikelihood metric based on the deployment of one or more vehicleairbags, the accident likelihood metric representing a likelihood thatthe vehicle will be involved in an accident during the drive.
 8. Themethod of claim 1 further comprising identifying an accident during thedrive based on the deployment of the one or more vehicle airbags.
 9. Themethod of claim 8 further comprising determining a severity of theaccident based on the deployment of the one or more vehicle airbags. 10.The method of claim 8 further comprising determining an angle ofcollision based on the deployment of the one or more vehicle airbags,the angle of collision being associated with the accident.
 11. Themethod of claim 8 further comprising determining a type of accidentassociated with the accident based on the deployment of the one or morevehicle airbags.
 12. The method of claim 11 wherein the type of accidentincludes a collision between the vehicle and another vehicle.
 13. Themethod of claim 11 wherein the type of accident includes a collisionbetween the vehicle and an environmental object.